Signal quality estimation for continuous phase modulation

ABSTRACT

A received continuous phase modulation (CPM) signal (which is formed with a set of pulse shaping functions) is approximated as a phase shift keying (PSK) modulated signal (which is formed with just the dominant pulse shaping function having the largest energy). Channel estimation and data detection are performed in accordance with the CPM-to-PSK approximation. A signal power estimate and a noise power estimate are obtained for the received CPM signal and have errors due to the CPM-to-PSK approximation. The difference Δ between the energy of the dominant pulse shaping function and the energy of the remaining pulse shaping functions is determined. An approximation error is estimated based on the signal power estimate and the difference Δ. A C/I estimate for the received CPM signal is computed based on the signal power estimate, the noise power estimate, and the approximation error estimate.

BACKGROUND

I. Field

The present invention relates generally to communication, and morespecifically to signal quality estimation in a wireless communicationsystem.

II. Background

In a wireless communication system, a transmitter first digitallyprocesses traffic/packet data to obtain coded data. The transmitter thenmodulates a carrier signal with the coded data to obtain a modulatedsignal that is more suitable for transmission via a wireless channel.The modulation may be performed based on various modulation schemes. Onemodulation scheme that is widely used throughout the world is continuousphase modulation (CPM). With CPM, the phase of the carrier signal ismodulated by the coded data in a continuous rather than abrupt manner.As a result, CPM has several desirable characteristics such as (1) aconstant envelope for the modulated signal, which allows the signal tobe transmitted using an efficient power amplifier, and (2) a compactspectrum for the modulated signal, which enables efficient utilizationof the available frequency spectrum.

A modulated signal generated with CPM (or simply, a CPM signal) has afairly complex waveform that can complicate the design of a receiverused to process the CPM signal. To simplify the receiver design, the CPMsignal may be approximated as a phase shift keying (PSK) modulatedsignal, as described below. This approximation for the CPM signal issufficiently accurate for many applications and is often used for a CPMreceiver.

A CPM receiver often needs to derive an estimate of the received signalquality. In general, signal quality (which is denoted as “C/I” herein)may be quantified by a carrier-to-interference ratio, a signal-to-noiseratio, a signal-to-noise-and-interference ratio, and so on. The C/Iestimate may be used for various purposes such as, for example, toselect an appropriate data rate for data transmission. The approximationof the CPM signal as a PSK modulated signal, while simplifying thereceiver design, results in an inaccurate C/I estimate for certainoperating conditions. The inaccurate C/I estimate can degrade systemperformance.

There is therefore a need in the art for techniques to derive a moreaccurate C/I estimate for a CPM signal.

SUMMARY

Methods and apparatus are presented herein to address the above statedneed. A CPM signal may be represented as (and formed with) a set ofpulse shaping functions, as described below. To simplify the receiverdesign, a CPM signal may be approximated as a PSK modulated signal thatis formed with just the “dominant” pulse shaping function, which is thepulse shaping function having the largest energy among all pulse shapingfunctions for the CPM signal. Channel estimation and data detection maybe more simply performed based on this CPM-to-PSK approximation.However, if the receiver processing is performed based on thisapproximation, then an estimate of the signal power in the received CPMsignal would contain mostly the power due to the dominant pulse shapingfunction, and the power due to the remaining pulse shaping functionswould be treated as noise instead of signal. This then results in aninaccurate C/I estimate for high C/I conditions.

A more accurate C/I estimate may be obtained for a received CPM signalby accounting for the CPM-to-PSK approximation. A signal power estimateand a noise power estimate may be obtained for the received CPM signal.These power estimates have errors due to the CPM-to-PSK approximation.The difference A between the energy of the dominant pulse shapingfunction and the energy of the remaining pulse shaping functions may bedetermined, for example, by theoretical derivation, computer simulation,or empirical measurement. An approximation error may be estimated basedon the signal power estimate and the difference Δ. The C/I estimate maythen be computed based on the signal power estimate, the noise powerestimate, and the approximation error estimate. The various computationsteps for the C/I estimate are described in further detail below. ThisC/I estimate is relatively accurate even with the CPM-to-PSKapproximation and may be used for various purposes such as, for example,selecting an appropriate data rate, scaling of bursts of data prior todecoding, and so on.

In one embodiment, a method is presented for estimating signal quality(C/I) in a communication system utilizing continuous phase modulation(CPM), the method comprising: estimating signal power in a received CPMsignal; estimating noise power in the received CPM signal; estimatingerror due to approximation of the received CPM signal with anothermodulation format different from CPM; and deriving a C/I estimate forthe received CPM signal based on the estimated signal power, theestimated noise power, and the estimated approximation error.

In another embodiment, a method is presented for estimating signalquality (C/I) in a Global System for Mobile Communications (GSM) system,comprising: estimating signal power in a received Gaussian minimum shiftkeying (GMSK) modulated signal; estimating noise power in the receivedGMSK modulated signal; estimating error due to approximation of thereceived GMSK modulated signal as a phase shift keying (PSK) modulatedsignal; and deriving a C/I estimate for the received. GMSK modulatedsignal based on the estimated signal power, the estimated noise power,and the estimated approximation error.

In another embodiment, apparatus is presented that is operable toestimate signal quality (C/I) in a wireless communication systemutilizing continuous phase modulation (CPM), comprising: a signalestimator operative to estimate signal power in a received CPM signal; anoise estimator operative to estimate noise power in the received CPMsignal; and a C/I estimator operative to estimate error due toapproximation of the received CPM signal with another modulation formatdifferent from CPM and to derive a C/I estimate for the received CPMsignal based on the estimated signal power, the estimated noise power,and the estimated approximation error.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and nature of the present invention will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings in which like reference charactersidentify correspondingly throughout and wherein:

FIG. 1 shows a transmitting entity and a receiving entity in a GlobalSystem for Mobile Communications (GSM) system;

FIG. 2 shows a Gaussian minimum shift keying (GMSK) modulator at thetransmitting entity;

FIG. 3 shows a GMSK demodulator at the receiving entity;

FIG. 4 shows a process to derive a C/I estimate for a CPM signal;

FIG. 5 shows a burst format used in GSM; and

FIGS. 6A and 6B show estimation of a channel impulse response based oncorrelation with truncated and full training sequences, respectively.

DETAILED DESCRIPTION

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs.

The C/I estimation techniques described herein may be used for varioustypes of CPM signals and for various wireless communication systems. Forclarity, these techniques are specifically described for a GMSKmodulated signal used in GSM.

FIG. 1 shows a block diagram of a transmitting entity 110 and areceiving entity 150 in a GSM system. Transmitting entity 110 may be abase station or a wireless device, and receiving entity 150 may also bea wireless device or a base station. At transmitting entity 110, atransmit (TX) data processor 112 receives, formats, codes, andinterleaves data based on one or more coding and interleaving schemesand provides a stream of input bits for a GMSK modulator 120. Modulator120 performs GMSK modulation on the input bits, as specified by GSM anddescribed below, and provides a GMSK modulated signal (or simply, a GMSKsignal). A transmitter unit (TMTR) 122 conditions (e.g., filters andamplifies) the GMSK signal to generate a radio frequency (RF) modulatedsignal that is transmitted via an antenna 124 to receiving entity 150.

At receiving entity 150, the RF modulated signal transmitted bytransmitting entity 110 is received by an antenna 152 and provided to areceiver unit (RCVR) 154. Receiver unit 154 conditions and digitizes thereceived GMSK signal and provides a stream of samples. A GMSKdemodulator 160 then processes the samples, estimates the C/I of thereceived GMSK signal, and provides demodulated data. A receive (RX) dataprocessor 162 deinterleaves and decodes the demodulated data to recoverthe data sent by transmitting entity 110. The processing by GMSKdemodulator 160 and RX data processor 162 is complementary to theprocessing performed by GMSK modulator 120 and TX data processor 112,respectively, at transmitting entity 110.

Controllers 130 and 170 direct operation at transmitting entity 110 andreceiving entity 150, respectively. Memory units 132 and 172 providestorage for program codes and data used by controllers 130 and 170,respectively.

FIG. 2 shows a block diagram of GMSK modulator 120 used to generate theGMSK signal at transmitting entity 110. Within GMSK modulator 120, adifferential encoder 212 receives and performs differential encoding onthe input bits {b(n)} and provides code symbols {a(n)}. Each code symbola(n) corresponds to one input bit b(n) and is generated based on thatinput bit b(n) and a preceding input bit b(n-1). For simplicity, eachinput bit b(n) and each code symbol a(n) has a value of either +1 or −1,i.e., b(n) ε {−1, +1} and a(n) ε {−1, +1}. A Gaussian lowpass filter 214receives and filters the code symbols. Filter 214 has a BT product of0.3, where B denotes the −3 dB bandwidth of the filter and T denotes onesymbol period. For BT=0.3, filter 214 provides a frequency pulse g(t)having a duration of approximately four symbol periods (4T) for eachcode symbol a(n). An integrator 216 integrates the output of filter 214and provides a modulating signal m(t), which contains a phase pulse φ(t)for each frequency pulse g(t) and thus for each code symbol a(n). Sincethe frequency pulse has a duration of appropriately 4 T, each codesymbol a(n) is sent over four symbol periods. Because of the filteringand integration as defined by GSM, the modulating signal m(t)transitions by at most 90° or π/2 in each symbol period T. The directionof the phase transition is either clockwise or counter-clockwise on asignal constellation and is determined by the values of the codesymbols. A phase modulator 218 receives the modulating signal m(t) fromintegrator 216 and a carrier signal from a local oscillator (LO)generator 220, modulates the carrier signal with the modulating signal,and provides the GMSK signal.

The GMSK signal, s(t), may be expressed in continuous time t as follows:$\begin{matrix}{{{s(t)} = {\exp( {j\lbrack {\theta_{0} + {\sum\limits_{n = {- \infty}}^{\infty}{{a(n)} \cdot {\varphi( {t - {nT}} )}}}} \rbrack} )}},{{{for}\quad{nT}} < t < {( {n + 1} )T}},} & {{Eq}\quad(1)}\end{matrix}$where φ(t) is the phase pulse/function determined by filter 214 andintegrator 216;

θ₀ is an arbitrary phase value; andexp(jx)=cos(x)+j sin(x).For simplicity, equation (1) shows a baseband representation for theGMSK signal, so that the “ωt” term for the angular frequency of thecarrier signal is omitted from equation (1). Equation (1) indicates thatthe code symbols {a(n)} are embedded in the phase of the GMSK signal.Equation (1) also indicates that the phase of the GMSK signal isobtained by multiplying each code symbol a(n) with a delayed version ofthe phase function, φ(t−nT), and summing the scaled and delayed phasefunctions for all code symbols. For simplicity, the parentheses “{ }”for {b(n)} and {a(n)} are omitted in the following description.

The phase-modulated GMSK signal shown in equation (1) may be representedas a superposition/sum of amplitude-modulated signals in discrete timen, as follows: $\begin{matrix}\begin{matrix}{{s(n)} = {{{a_{0}(n)} \otimes {c_{0}(n)}} + {{a_{1}(n)} \otimes {c_{1}(n)}} + \ldots}} \\{{= {\sum\limits_{i = 0}{{a_{i}(n)} \otimes {c_{i}(n)}}}},}\end{matrix} & {{Eq}\quad(2)}\end{matrix}$where {circle around (×)} denotes a convolution operation;

c_(i)(n) denotes the i-th pulse shaping function; and

a_(i)(n) denotes the input symbols for pulse shaping function c_(i)(n).

Equation (2) indicates that the complex GMSK signal, s(n), may beexpressed as a sum of amplitude-modulated signals. Eachamplitude-modulated signal is generated by convolving a pulse shapingfunction c_(i)(n) with its corresponding input symbols a_(i)(n). ForGMSK, there are eight pulse shaping functions, which are denoted asc_(i)(n) for i=0, 1, . . . 7. Of these eight functions, c₀(n) is thedominant pulse shaping function and is much larger than the remainingpulse shaping functions. The input symbols a_(i)(n) for each pulseshaping function may be derived from the code symbols a(n) based on aknown transformation associated with that function. There is aone-to-one mapping between a_(i)(n) and a(n) for each pulse shapingfunction. For GMSK, differential encoding is performed on the input bitsb(n) to obtain the code symbols a(n), and the input symbols a₀(n) forthe dominant pulse shaping function c₀(n) may be expressed as:a ₀(n)=j ^(n) ·b(n),   Eq (3)where j=√{square root over (−1)}. The decomposition of a CPM signal toamplitude-modulated pulse (AMP) representation, the pulse shapingfunctions, and the generation of the input symbols for these pulseshaping functions are described by P. A. Laurent in a paper entitled“Exact and Approximate Construction of Digital Phase Modulations bySuperposition of Amplitude Modulated Pulses (AMP),” IEEE Transactions onCommunications, Vol. COM-34, No. 2, February 1986.

FIG. 3 shows an embodiment of GMSK demodulator 160 used to process areceived GMSK signal at receiving entity 150. The GMSK signal, s(n), istransmitted via a channel model 310 that includes a propagation channel312 and a summer 314 for additive noise. Propagation channel 312 has animpulse response of p(n), which includes the effects of the wirelesschannel as well as any transmit pulse shaping performed at transmittingentity 110 and any pre-filtering performed at receiving entity 150. The“channel” noise w(n) is the total noise in the received GMSK signal andincludes noise from the wireless channel as well as receiver noise. Thereceived GSM signal, r(n), at the input of GMSK demodulator 160 may beexpressed as: $\begin{matrix}\begin{matrix}{{r(n)} = {{{s(n)} \otimes {p(n)}} + {w(n)}}} \\{{= {{\sum\limits_{i = 0}{{a_{i}(n)} \otimes {{\overset{\Cup}{c}}_{i}(n)}}} + {w(n)}}},}\end{matrix} & {{Eq}\quad(4)}\end{matrix}$where {hacek over (c)}_(i)(n)=c_(i)(n){circle around (×)}p(n) is thei-th pulse shaping function observed by the received GMSK signal, r(n).The function {hacek over (c)}_(i)(n) is obtained by convolving theoriginal pulse shaping function, c_(i)(n), with the impulse responsep(n) for the propagation channel.

Within GMSK demodulator 160, a rotator 330 performs phase rotation onthe received GSM signal, r(n), to undo the effect of the differentialencoding by GMSK modulator 120. Rotator 330 rotates each successivesample in the received GMSK signal by −90° (e.g., rotate the firstsample by −90°, the second sample by −180°, the third sample by −270°,the fourth sample by 0°, the fifth sample by −90°, and so on). Therotated GMSK signal, {tilde over (r)}(n), may be expressed as:$\begin{matrix}{{{\overset{\sim}{r}(n)} = {{\sum\limits_{i}{{b_{i}(n)} \otimes {{\overset{\sim}{c}}_{i}(n)}}} + {\overset{\sim}{w}(n)}}},} & {{Eq}\quad(5)}\end{matrix}$where {tilde over (c)}_(i)(n)=j^(−n)·{hacek over(c)}_(i)(n)=j^(−n)·c_(i)(n){circle around (×)}p(n) is the i-th“effective” pulse shaping function for the rotated GMSK signal, {tildeover (r)}(n);

-   -   b_(i)(n)=j^(−n)·a_(i)(n) denotes the input symbols for function        {tilde over (c)}_(i)(n); and    -   {tilde over (w)}(n) is a rotated version of the channel noise        w(n).        The input symbols b_(i)(n) for the i-th effective pulse shaping        function, {tilde over (c)}_(i)(n), are generated by rotating the        input symbols a_(i)(n) for the i-th original pulse shaping        function, c_(i)(n). The rotation results in the input symbols        for {tilde over (c)}₀(n) being equal to the input bits into the        GMSK modulator, or b₀(n)=j^(−n)·a₀(n)=b(n), which is obtained        using a₀(n)=j^(n)·b(n) in equation (3).

To simplify the receiver processing, the rotated GMSK signal may beapproximated as a binary phase shift keying (BPSK) signal, as follows:{circumflex over (r)}(n)=b ₀(n){circle around (×)}{tilde over (c)}₀(n)+{tilde over (w)}(n),   Eq (6)where {circumflex over (r)}(n) is the estimated GMSK signal. TheGMSK-to-BPSK approximation shown in equation (6) relies on the fact thatthe dominant pulse shaping function, c₀(n), is much larger than theremaining pulse shaping functions. In this case, a single term for{tilde over (c)}₀(n) in the summation for the rotated GMSK signal,{tilde over (r)}(n), in equation (5) is used for the estimated GMSKsignal, {circumflex over (r)}(n), in equation (6). The estimated GMSKsignal, {circumflex over (r)}(n), is a good approximation of the rotatedGMSK signal, {tilde over (r)}(n), when c₀(n) is much larger than allother pulse shaping functions. Since the estimated GMSK signal,{circumflex over (r)}(n), contains only one pulse shaping function,channel estimation and data detection are simplified.

A channel estimator 340 receives the rotated GMSK signal, {tilde over(r)}(n), and derives an estimate of the channel impulse response, ĥ(n),observed by the rotated GMSK signal. The channel estimation is performedin accordance with the GMSK-to-BPSK approximation shown in equation (6),so that input symbols b₀(n) for a known training sequence are used toderive the channel impulse response estimate, as described below.Because of this GMSK-to-BPSK approximation, the channel impulse responseestimate approximates and resembles the dominant effective pulse shapingfunction, or ĥ(n)≈{tilde over (c)}₀(n).

A data detector 350 receives the rotated GMSK signal, {tilde over(r)}(n), and the channel impulse response estimate, ĥ(n), and performsdata detection to recover the input symbols b₀(n) for the dominant pulseshaping function. Data detector 350 may implement a maximum likelihoodsequence estimator (MLSE) that determines a sequence of symbols that ismost likely to have been transmitted given the rotated GMSK signal,{tilde over (r)}(n), and the channel impulse response estimate, ĥ(n).Data detection for GSM is known in the art and not described herein.Data detector 350 provides hard decisions/bit estimates {circumflex over(b)}₀(n), which are estimates of the input symbols b₀(n). The bitestimates {circumflex over (b)}₀(n) are deinterleaved and decoded by RXdata processor 162 to obtain decoded data (not shown in FIG. 3).

To obtain a C/I estimate, a signal estimator 360 receives and convolvesthe channel impulse response estimate, ĥ(n), with the bit estimates,{circumflex over (b)}₀(n), to generate a reconstructed signal. Thisreconstructed signal is an estimate of the signal component in therotated GMSK signal due to the dominant pulse shaping function. A summer362 receives and subtracts the reconstructed signal from the rotatedGMSK signal to obtain a noise estimate, ŵ(n), as follows:

ŵ(n)={tilde over (r)}( n)−{circumflex over (b)} ₀(n){circle around(×)}ĥ(n),   Eq (7)

where ŵ(n) is an estimate of the noise {tilde over (w)}(n) in therotated GMSK signal. Although not shown in FIG. 3 for simplicity, therotated GMSK signal is typically delayed prior to summer 362 to betime-aligned with the reconstructed signal.

A signal power estimator 344 computes an estimate of the signal power,P_(signal), as follows: $\begin{matrix}{{P_{signal} = {\sum\limits_{n = 0}^{M - 1}{{\hat{h}(n)}}^{2}}},} & {{Eq}\quad(8)}\end{matrix}$where M is the number of taps for the channel impulse response estimate,ĥ(n). M is also the length of the channel impulse response estimate. Thesignal power estimate may also be derived based on the output fromsignal estimator 360, or P_(signal)=E[|{circumflex over (b)}₀(n){circlearound (×)}ĥ(n)|²], where E [x] is the expected value of x. A noisepower estimator 364 computes an estimate of the noise power, P_(noise),as follows: $\begin{matrix}{P_{noise} = {{\frac{1}{N} \cdot {\sum\limits_{n = 0}^{N - 1}{{\hat{w}(n)}}^{2}}} = {{E\lbrack {{\hat{w}(n)}}^{2} \rbrack}.}}} & {{Eq}\quad(9)}\end{matrix}$In GSM, data is transmitted in bursts, with each burst carrying asequence of N input bits, or b(0) . . . b(N-1) . The noise powerestimate may be computed for each burst.

A C/I estimator 370 may then compute a C/I estimate (or a burst SNRestimate) for the received GMSK signal, as follows: $\begin{matrix}{{C/I_{bpsk}} = {\frac{P_{signal}}{P_{noise}} = {\frac{\sum\limits_{n = 0}^{M - 1}{{\hat{h}(n)}}^{2}}{E\lbrack {{\hat{w}/(n)}}^{2} \rbrack}.}}} & {{Eq}\quad(10)}\end{matrix}$Equation (10) indicates that, assuming the error due to the GMSK to BPSKapproximation is negligible, the accuracy of the C/I estimate isdependent on the accuracy of the channel impulse response estimate,ĥ(n). This is because the signal and noise power estimates are bothderived based on ĥ(n).

The C/I estimate obtained as shown in equation (10) is relativelyaccurate for low C/I conditions (e.g., for a received C/I of 12 dB orlower). However, this C/I estimate saturates for high C/I conditions(e.g., for a received C/I of 16 dB or more). The value to which the C/Iestimate saturates is dependent on the manner in which the channelimpulse response is estimated.

The saturation of the C/I estimate in equation (10) is due to theapproximation of the received GMSK signal with just the dominant pulseshaping function {tilde over (c)}₀(n), as shown in equation (6). Forhigh C/I conditions, channel estimator 340 provides an accurate channelimpulse response estimate for the estimated GMSK signal, so thatĥ(n)→{tilde over (c)}₀(n). Also for high C/I conditions, data detector350 provides accurate bit estimates based on the rotated GMSK signal, sothat {circumflex over (b)}₀(n)→b₀(n). The noise estimate, ŵ(n), thenincludes the channel noise as well as signal components for all pulseshaping functions except for the dominant pulse shaping function. Thenoise estimate ŵ(n) may thus be expressed as:ŵ(n)≈b ₁(n){circle around (×)}{tilde over (c)} ₁(n)+b ₂(n){circle around(×)}{tilde over (c)} ₂(n)+ . . . +{tilde over (w)}( n).   Eq (11)The “≈” sign in equation ( 11) may be replaced with an “=” sign ifĥ(n)={tilde over (c)}₀(n) and {circumflex over (b)}₀(n)=b₀(n). The noisepower estimate with the approximation error may then be expressed as:$\begin{matrix}{P_{noise} = {{E\lbrack {{\hat{w}(n)}}^{2} \rbrack} = {{\sum\limits_{i \geq 1}{\sum\limits_{n}{{{\overset{\sim}{c}}_{i}(n)}}^{2}}} + {{E\lbrack {{\overset{\sim}{w}(n)}}^{2} \rbrack}.}}}} & {{Eq}\quad(12)}\end{matrix}$The C/I estimate in equation (10) may then be expressed as:$\begin{matrix}{{{C/I_{bpsk}} = {\frac{\sum\limits_{n = 0}^{M - 1}{{\hat{h}(n)}}^{2}}{E\lbrack {{\overset{\sim}{w}(n)}}^{2} \rbrack} \approx \frac{\sum\limits_{n = 0}^{L - 1}{{{\overset{\sim}{c}}_{0}(n)}}^{2}}{{\sum\limits_{i \geq 1}{\sum\limits_{n}{{{\overset{\sim}{c}}_{i}(n)}}^{2}}} + {E\lbrack {{\overset{\sim}{w}(n)}}^{2} \rbrack}}}},} & {{Eq}\quad(13)}\end{matrix}$where L is the length of the dominant effective pulse shaping function,{tilde over (c)}₀(n).

Equation (13) indicates that the approximation of the received GMSKsignal with just the dominant pulse shaping function, {tilde over(c)}₀(n), results in the remaining pulse shaping functions {tilde over(c)}₁(n), {tilde over (c)}₂(n), and so on, being treated as noiseinstead of as signal. Consequently, even with no channel noise, orE[|{tilde over (w)}(n)|²]=0, the C/I estimate saturates at C/I_(sat),which is: $\begin{matrix}{{{C/I_{sat}} = {\frac{\sum\limits_{n = 0}^{L - 1}{{{\overset{\sim}{c}}_{0}(n)}}^{2}}{\sum\limits_{i \geq 1}{\sum\limits_{n}{{{\overset{\sim}{c}}_{i}(n)}}^{2}}} = \frac{P_{dom}}{P_{rem}}}},} & {{Eq}\quad(14)}\end{matrix}$where P_(dom) is the power of the dominant effective pulse shapingfunction; and

P_(rem) is the power of the remaining effective pulse shaping functions.

Computer simulation indicates that C/I_(sat) is approximately 20 dB forGMSK. The power P_(rem) may be viewed as an error due to GMSK-to-BPSKapproximation. At low

C/I conditions, the approximation error is less than the channel noiseand has negligible impact on the accuracy of the C/I estimate. However,at high C/I conditions, the approximation error is larger than thechannel noise and causes the C/I estimate to saturate at C/I_(sat).

The true C/I for the received GMSK signal may be expressed as:$\begin{matrix}{\begin{matrix}{{C/I_{true}} = \frac{E\quad\lbrack {{{signal}\quad{component}\quad{in}\quad r\quad(n)}}^{2} \rbrack}{E\lbrack {{w\quad(n)}}^{2} \rbrack}} \\{= \frac{E\quad\lbrack {{{signal}\quad{component}\quad{in}\quad\overset{\sim}{r}\quad(n)}}^{2} \rbrack}{E\lbrack {{\overset{\sim}{w}\quad(n)}}^{2} \rbrack}}\end{matrix}.} & {{Eq}\quad(15)}\end{matrix}$The second equality in equation (15) follows from the fact that therotation operation by rotator 330 does not change the signal power ornoise power. The power of the signal components is the same in thereceived GMSK signal, r(n), and the rotated GMSK signal, {tilde over(r)}(n). Furthermore, the channel noise power is the same as the rotatednoise power, or E[|w(n)|²]=E[|{tilde over (w)}(n)|²]. For an additivewhite Gaussian noise (AWGN) channel with a flat frequency response,equation (15) may be expressed as: $\begin{matrix}{\begin{matrix}{{C/I_{true}} = \frac{E\quad\lbrack {{{signal}\quad{component}\quad{in}\quad\overset{\sim}{r}\quad(n)}}^{2} \rbrack}{E\lbrack {{\overset{\sim}{w}\quad(n)}}^{2} \rbrack}} \\{= \frac{{\sum\limits_{n = 0}^{L - 1}\quad{{{\overset{\sim}{c}}_{0}(n)}}^{2}} + {\sum\limits_{i \geq 1}\quad{\sum\limits_{n}\quad{{{\overset{\sim}{c}}_{i}(n)}}^{2}}}}{E\lbrack {{\overset{\sim}{w}\quad(n)}}^{2} \rbrack}}\end{matrix}.} & {{Eq}\quad(16)}\end{matrix}$As shown in equations (13) and (16), the saturation of the C/I estimateresults from treating the signal power in the remaining pulse shapingfunctions as a noise component in equation (13) instead of as a signalcomponent in equation (16).

A more accurate C/I estimate, C/I_(est), may be obtained for thereceived GMSK signal by accounting for the GMSK-to-BPSK approximationerror, as follows: $\begin{matrix}\begin{matrix}{{C/I_{est}} = \frac{{\sum\limits_{n = 0}^{M - 1}\quad{{\hat{h}\quad(n)}}^{2}} + P_{error}}{{E\lbrack {{\hat{w}\quad(n)}}^{2} \rbrack} - P_{error}}} \\{{\approx \frac{{\sum\limits_{n = 0}^{L - 1}\quad{{{\overset{\sim}{c}}_{0}(n)}}^{2}} + {\sum\limits_{i \geq 1}\quad{\sum\limits_{n}\quad{{{\overset{\sim}{c}}_{i}(n)}}^{2}}}}{E\lbrack {{\overset{\sim}{w}\quad(n)}}^{2} \rbrack}},}\end{matrix} & {{Eq}\quad(17)}\end{matrix}$where P_(error) is an estimate of P_(rem). Equation (17) is obtainedusing equations (13) and (16). As noted above, P_(rem) is approximately20 dB smaller than P_(dom). Also, P_(dom) may be assumed to be equal tothe signal power, or $\begin{matrix}{P_{dom} = {{{\sum\limits_{n = 0}^{L - 1}\quad{{{\overset{\sim}{c}}_{0}(n)}}^{2}} \approx {\sum\limits_{n = 0}^{M - 1}\quad{{\hat{h}\quad(n)}}^{2}}} = {P_{signal}.}}} & {{Eq}\quad(18)}\end{matrix}$In this case, P_(error), may be estimated based on the signal power, asfollows: $\begin{matrix}{{P_{error} = {{10^{{- \Delta}/10} \times {\sum\limits_{n = 0}^{L - 1}{{\hat{h}\quad(n)}}^{2}}} \approx {0.01 \times {\sum\limits_{n = 0}^{M - 1}\quad{{\hat{h}\quad(n)}}^{2}}}}},} & {{Eq}\quad(19)}\end{matrix}$where Δ is the difference between P_(dom) and P_(rem)(in dB) and isestimated as Δ≈20 dB in equation (19) for GMSK.

The assumption shown in equation (18) is generally more accurate forhigh C/I conditions than low C/I conditions because the channel impulseresponse estimate is more accurate for high C/Is. Consequently, theaccuracy of the estimate shown in equation (18) is dependent on the C/Iof the received GMSK signal. However, an appropriate value may beselected for the parameter Δ such that an accurate C/I estimate may beobtained for a wide range of received C/Is.

As shown in equation (17) the approximation error estimate, P_(error),is subtracted from the noise power estimate E[|ŵ(n)|²] in thecomputation of C/I_(est). The result of this subtraction may be zero ora negative value due to various reasons such as, for example, the use ofa finite number of samples to estimate the signal power and noise power.If this occurs, then C/I_(est) may be set to a predetermined maximum C/Ivalue, or C/I_(est)=C/ I_(max).

FIG. 4 shows a flow diagram of a process 400 to derive a C/I estimatefor a CPM signal (e.g., a GMSK signal). Initially, an appropriate valueis determined for the parameter Δ, which indicates the differencebetween the energy of the dominant effective pulse shaping function andthe energy of the remaining effective pulse shaping functions (block412). For any CPM scheme/format that may be selected for use, a set ofpulse shaping functions {c_(i)(n)} for that CPM scheme/format may bedetermined based on applicable parameter values for that CPMscheme/format (e.g., the BT value for the Gaussian lowpass filter) andas described by Laurent. An effective pulse shaping function {tilde over(c)}_(i)(n) may be derived for each pulse shaping function c_(i)(n)based on the impulse response p(n) of the propagation channel, or {tildeover (c)}_(i)(n)=j^(−n)·c_(i)(n){circle around (×)}p(n). The parameter Δmay then be set to the ratio of the energy of the dominant effectivepulse shaping function, {tilde over (c)}₀(n), to the energy of theremaining effective pulse shaping functions, {tilde over (c)}_(i)(n) fori=1, 2, . . . , as follows: $\begin{matrix}{\Delta = {{10\quad{\log_{10}( \frac{\sum\limits_{n = 0}^{L - 1}\quad{{{\overset{\sim}{c}}_{0}(n)}}^{2}}{\sum\limits_{i \geq 1}\quad{\sum\limits_{n}\quad{{{\overset{\sim}{c}}_{i}(n)}}^{2}}} )}} \approx {10\quad{{\log_{10}( \frac{\sum\limits_{n = 0}^{M - 1}\quad{{\hat{h}\quad(n)}}^{2}}{\sum\limits_{i \geq 1}\quad{\sum\limits_{n}\quad{{{\overset{\sim}{c}}_{i}(n)}}^{2}}} )}.}}}} & {{Eq}\quad(20)}\end{matrix}$For an AWGN channel, the impulse response p(n) contains a single tap ofunit magnitude, and {tilde over (c)}_(i)(n)=j^(−n)·c_(i)(n). For amultipath channel, the impulse response p(n) contains multiple taps. Thevalue for Δ may be determined via computer simulation, empiricalmeasurement, and so on, and (for simplicity) by assuming using the samedelta value as that for an AWGN channel.

The signal power for the received CPM signal is then estimated based onan approximation of the received CPM signal as a PSK signal (block 414).The signal power estimate, P_(signal), may be computed based on thechannel impulse response estimate, as shown in equation (8), or based ona reconstructed signal. The noise in the received CPM signal is computedand the noise power is estimated based on the CPM-to-PSK approximation(block 416). For example, the noise estimate may be computed as shown inequation (7) and the noise power estimate, P_(noise), may be computed asshown in equation (9). The CPM-to-PSK approximation error is thenestimated based on the signal power estimate, P_(signal), and theparameter Δ, for example, as P_(error)=10^(−Δ/10)×P_(signal) (block418). The C/I estimate for the received CPM signal is then derived basedon the signal power estimate, P_(signal), a the noise power estimate,P_(noise), and the approximation error estimate, P_(error). (block 420),as follows: $\begin{matrix}{{C/I_{est}} = {\frac{P_{signal} + P_{error}}{P_{noise} - P_{error}}.}} & {{Eq}\quad(21)}\end{matrix}$The C/I estimate may further be post-processed (e.g., filtered overmultiple bursts) to obtain a more reliable estimate of the received C/I.

Process 400 may be used to derive an accurate C/I estimate for any CPMsignal that is approximated as a PSK signal. In general, the receivedCPM signal may be approximated with one or multiple pulse shapingfunctions. The parameter Δ would then indicate the difference betweenthe energy of all pulse shaping functions used to appropriate the PSKsignal and the energy of all remaining pulse shaping functions.

As noted above, the channel impulse response estimate impacts both thesignal power estimate and the noise power estimate, both of which inturn impact the C/I estimate. The channel impulse response may beestimated in various manners. Several channel estimation schemes arespecifically described below for GSM.

FIG. 5 shows a burst format 500 used in GSM for transmission of trafficdata. Each burst includes two tail bit (TB) fields, two data fields, atraining sequence field, and a guard period (GP). The number of bits foreach field is shown inside the parentheses. Each burst is transmitted inone time slot, which is 0.577 msec in GSM.

GSM defines eight different training sequences (or midambles) that maybe sent in the training sequence field. Each training sequence contains26 bits and is defined such that the first five bits (labeled as ‘A’)are repeated and the second five bits (labeled as ‘B’) are alsorepeated, as shown in FIG. 5. Each training sequence u(n) is alsodefined such that the correlation of that sequence with a 16-bittruncated version of that sequence, v(n), is equal to (1) 16 for a zerotime shift (as shown in FIG. 5), (2) zero for time shifts of ±1, ±2, ±3,±4, and ±5, and (3) a zero or non-zero value for any other time shift.For an ideal training sequence, the autocorrelation is a maximum valuewith no time shift and zero for all other time shifts.

FIG. 6A shows the estimation of a channel impulse response based oncorrelation with the 16-bit truncated training sequence v(n). Theestimated GSM signal, {circumflex over (r)}(n), for the trainingsequence portion may be expressed as: $\begin{matrix}{{{{\hat{r}\quad(n)} \approx {{h\quad{(n) \otimes {u_{0}(n)}}} + {\overset{\sim}{w}\quad(n)}}} = {{\sum\limits_{i = 0}^{M - 1}\quad{h\quad{(i) \cdot {u_{0}( {n - i} )}}}} + {\overset{\sim}{w}\quad(n)}}},} & {{Eq}\quad(22)}\end{matrix}$where u₀(n) represents the input symbols for the dominant pulse shapingfunction c₀(n), which are derived based on the training sequence bitsu(n) and using the same transformation as for b₀(n). Because of theGMSK-to-BPSK approximation, the estimated GSM signal, {circumflex over(r)}(n), for the training sequence may be viewed as containing only theinput symbols u₀(n) for the dominant pulse shaping function c₀(n).

The correlation between the samples in the rotated GMSK signal, {tildeover (r)}(n), and the truncated training sequence, v(n), may beexpressed as: $\begin{matrix}\begin{matrix}{{z\quad(n)} = {\sum\limits_{i = 0}^{K - 1}\quad{\overset{\sim}{r}\quad{( {n + i} ) \cdot {v_{0}^{*}(i)}}}}} \\{\approx {{\sum\limits_{j = 0}^{M - 1}\quad{h\quad{(j) \cdot \lbrack {\sum\limits_{i = 0}^{K - 1}\quad{{u_{0}( {n + i - j} )} \cdot {v_{0}^{*}(i)}}} \rbrack}}} + {w^{\prime}(n)}}} \\{{\approx {{K \cdot h}\quad(n)}},}\end{matrix} & {{Eq}\quad(23)}\end{matrix}$where v₀(i) is a 16-bit truncated portion of the 26-bit sequence u₀(n),“*” denotes a complex conjugate, K=16, and w′(n) is post-processed noiseobtained from processing {tilde over (w)}(n) in {tilde over (r)}(n) withv*₀(i). Equation (23) indicates that the correlation result z(n) foreach time offset n is approximately equal to a scaled version of thechannel tap at that time offset. The correlation result z(n) may thus beused as the channel tap estimate for that time offset. The correlationmay be performed for different time offsets in a small window (e.g., of10 bit periods) centered at the expected peak in the correlation, whichis the start of the first ‘B’ portion. Because the autocorrelation ofthe truncated training sequence is zero for time offsets of +5 to −5 bitperiods, the channel tap estimates do not contain correlation error forthese time offsets.

FIG. 6B shows the estimation of a channel impulse response based oncorrelation with the 26-bit full training sequence, u(n). The samples inthe rotated GMSK signal, {tilde over (r)}(n), and the training sequenceinput symbols, u₀(n), for the dominant pulse shaping function arecorrelated for different time offsets in a small window (e.g., of 10 bitperiods) centered at the expected peak in the correlation, which in thiscase is the start of the first ‘A’ portion. The correlation result z(n)for each time offset n is approximately equal to a scaled version of thechannel tap at that time offset. The use of a longer sequence for thecorrelation allows more energy to be collected for the channel tapestimates. However, because the autocorrelation of the full trainingsequence, u(n), is zero for only certain specific time offsets, thechannel tap estimates contain correlation errors at time offsets withnon-zero autocorrelation.

The correlation as shown in FIG. 6A or 6B provides correlation resultsz(n) for different time offsets. These correlation results may be useddirectly as the channel taps for the channel impulse response estimate.Alternatively, the correlation results may be post-processed, forexample, using a least mean square (LMS) procedure, a Weiner Hopfprocedure, or some other procedure that can account for the non-idealautocorrelation properties of the training sequences, u(n), used in GSM.The channel tap estimates may also be processed, for example, bydiscarding channel taps with power less than a predetermined threshold.

The C/I estimate derived as described herein may be advantageously usedfor various purposes. Some exemplary uses are described below.

The C/I estimate may be used to select an appropriate data rate (orsimply, a rate) for data transmission. For example, in GSM, an AdaptiveMulti-Rate (AMR) vocoder is used to encode speech data. The AMR vocodersupports multiple coder/decoder (codec) modes, and each codec mode isassociated with a specific rate. For example, AMR Full Rate supports 8codec modes for 8 rates of 12.2, 10.2, 7.95, 7.4, 6.7, 5.9, 5.15, and4.75 kbps. The codec mode for 12.2 kbps has the highest rate and lowestcompression ratio, and the AMR vocoder generates 12.2 kbps ×20 msec=244bits for each 20 msec block of speech data. Each 20 msec block of datais encoded to obtain 456 code bits that can be transmitted in a fixedframe structure used in GSM. The AMR vocoder provides different amountsof redundancy for different codec modes. The codec mode with the highestrate provides the least amount of redundancy, which then results in theleast error correction capability. The converse is true for the codecmode with the lowest rate. Codec modes for higher rates are thustypically used for good channel conditions, and codec modes for lowerrates are usually used for bad channel conditions. This adaptationbetween data coding and channel conditions is used to achieve a goodtradeoff between voice quality and communication error rates.

A receiving entity (which may be a wireless device or a base station)may derive a C/I estimate for each burst, as described above, and usethis C/I estimate as a channel quality indicator. C/I estimates formultiple bursts may be filtered, e.g., based on a running average. The(filtered or unfiltered) C/I estimate may be compared against a set ofC/I thresholds, e.g., on a burst-by-burst basis. Based on the result ofthe comparison, the receiving entity may determine whether to requestthe transmitting entity to use a different codec mode that can achieve abetter tradeoff between voice quality and probability of reliabletransmission. For example, if the C/I estimate exceeds a predeterminedC/I threshold, then the channel condition may be considered to besufficiently improved, and the receiving entity may request a codec modethat performs less compression and thus provides higher voice quality.This benefit is obtained at the expense of less error protection, whichmay be acceptable because of the improved channel condition. A C/Ithreshold as high as 28 dB may be used for certain instances. Thetechniques described herein allow the receiving entity to estimate andreport high received C/Is, which allows for the selection of codec modeswith higher rates for good channel conditions.

C/I estimates may also be used to scale multiple bursts received via awireless channel, prior to decoding. For GSM, the transmitting entitymay partition each block of data into multiple subblocks and maytransmit each subblock as a burst in one time slot. The wireless channelmay distort each transmitted burst with a different channel response andmay further degrade each transmitted burst with different amount ofnoise. The receiving entity receives the transmitted bursts andprocesses each received burst to obtain soft-decision metrics (orsimply, “soft metrics”) for the burst. A soft metric is a multi-bitvalue obtained by the receiving entity for a single-bit (or “hard”)value sent by the transmitting entity. The receiving entity may scalethe soft metrics for each received burst for a given data block with ascaling factor that is determined based on the C/I estimate for thatburst. The scaling of each burst based on its C/I estimate allowsdifferent bursts to be given appropriate weight in the decoding processbased on their C/I estimates.

A C/I estimate may also be used as a bad frame indicator (BFI) todetermine whether a received burst is “good” or “bad”. For example, aC/I estimate may be derived for each burst and compared against a C/Ithreshold. The burst may be declared “good” if the C/I estimate is abovethe C/I threshold and other criteria (if any) are met and “bad”otherwise. The burst may be further decoded if deemed to be “good” anddiscarded otherwise. The bad frame indicator may also be used for otherpurposes. For example, if the burst is deemed to be “good”, thenpertinent information may be collected and used for automatic frequencycontrol (AFC), time tracking, and so on. Different C/I thresholds may beused for different purposes.

Some exemplary uses of the C/I estimate have been described above. TheC/I estimate may also be used for other purposes, and this is within thescope of the invention.

The C/I estimation techniques described herein may be implemented byvarious means. For example, these techniques may be implemented inhardware, software, or a combination thereof. For a hardwareimplementation, the processing units used to perform C/I estimation(e.g., the processing units shown in FIG. 3) may be implemented withinone or more application specific integrated circuits (ASICs), digitalsignal processors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,other electronic units designed to perform the functions describedherein, or a combination thereof.

For a software implementation, the C/I estimation techniques may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. The software codes may be storedin a memory unit (e.g., memory unit 172 in FIG. 1) and executed by aprocessor (e.g., controller 170). The memory unit may be implementedwithin the processor or external to the processor, in which case it canbe communicatively coupled to the processor via various means as isknown in the art.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

1. A method of estimating signal quality (C/I) in a communication systemutilizing continuous phase modulation (CPM), comprising: estimatingsignal power in a received CPM signal; estimating noise power in thereceived CPM signal; estimating error due to approximation of thereceived CPM signal with another modulation format different from CPM;and deriving a C/I estimate for the received CPM signal based on theestimated signal power, the estimated noise power, and the estimatedapproximation error.
 2. The method of claim 1, wherein the received CPMsignal is approximated as a phase shift keying (PSK) modulated signal,and wherein the signal power and the noise power are estimated based onthe approximation of the received CPM signal as a PSK modulated signal.3. The method of claim 1, wherein the received CPM signal is a Gaussianminimum shift keying (GMSK) modulated signal and is approximated as abinary phase shift keying (BPSK) modulated signal.
 4. The method ofclaim 1, wherein the received CPM signal is formed based on a pluralityof pulse shaping functions, and wherein the received CPM signal isapproximated with a dominant pulse shaping function having largestenergy among the plurality of pulse shaping functions.
 5. The method ofclaim 4, wherein the approximation error is estimated based on aparameter indicative of a ratio of energy of the dominant pulse shapingfunction to energy of remaining ones of the plurality of pulse shapingfunctions.
 6. The method of claim 5, wherein the parameter is set toapproximately 20 decibels (dB).
 7. The method of claim 5, wherein theapproximation error is further estimated based on the estimated signalpower.
 8. The method of claim 1, further comprising: deriving a channelimpulse response estimate based on the received CPM signal, and whereinthe signal power and the noise power are estimated based on the channelimpulse response estimate.
 9. The method of claim 8, wherein theestimated signal power is equal to energy of the channel impulseresponse estimate.
 10. The method of claim 8, wherein the estimating thenoise power comprises: detecting bits in the received CPM signal;generating a reconstructed signal based on the detected bits and thechannel impulse response estimate; deriving a noise estimate based onthe received CPM signal and the reconstructed signal; and computingpower of the noise estimate to obtain the estimated noise power.
 11. Themethod of claim 8, wherein the channel impulse response estimate isderived based on the approximation of the received CPM signal withanother modulation format.
 12. The method of claim 1, wherein thechannel impulse response estimate is derived by correlating the receivedCPM signal with a known training sequence.
 13. The method of claim 12,wherein the received CPM signal is formed based on a plurality of pulseshaping functions, and wherein the known training sequence is for adominant pulse shaping function having largest energy among theplurality of pulse shaping functions.
 14. The method of claim 1, furthercomprising: comparing the C/I estimate against one or more C/Ithresholds; and selecting a data rate based on a result of thecomparison.
 15. The method of claim 14, wherein at least one of the oneor more C/I thresholds is 20 decibels (dB) or higher.
 16. The method ofclaim 1, further comprising: obtaining a plurality of bursts of datafrom the received CPM signal; deriving a C/I estimate for each of theplurality of bursts; and scaling each burst of data based on the C/Iestimate derived for the burst.
 17. The method of claim 1, furthercomprising: comparing the C/I estimate against a C/I threshold; anddeclaring a block of data obtained from the received CPM signal as goodor bad based on a result of the comparison.
 18. The method of claim 1,further comprising: filtering C/I estimates derived for a plurality oftime intervals to obtain filtered C/I estimates having greaterreliability.
 19. The method of claim 1, wherein the communication systemis a Global System for Mobile Communications (GSM) system.
 20. A methodof estimating signal quality (C/I) in a Global System for MobileCommunications (GSM) system, comprising: estimating signal power in areceived Gaussian minimum shift keying (GMSK) modulated signal;estimating noise power in the received GMSK modulated signal; estimatingerror due to approximation of the received GMSK modulated signal as aphase shift keying (PSK) modulated signal; and deriving a C/I estimatefor the received GMSK modulated signal based on the estimated signalpower, the estimated noise power, and the estimated approximation error.21. An apparatus operable to estimate signal quality (C/I) in a wirelesscommunication system utilizing continuous phase modulation (CPM),comprising: a signal estimator operative to estimate signal power in areceived CPM signal; a noise estimator operative to estimate noise powerin the received CPM signal; and a C/I estimator operative to estimateerror due to approximation of the received CPM signal with anothermodulation format different from CPM and to derive a C/I estimate forthe received CPM signal based on the estimated signal power, theestimated noise power, and the estimated approximation error.
 22. Theapparatus of claim 21, wherein the received CPM signal is approximatedas a phase shift keying (PSK) modulated signal, and wherein the signalpower and the noise power are estimated based on the approximation ofthe received CPM signal as a PSK modulated signal.
 23. The apparatus ofclaim 21, wherein the received CPM signal is formed based on a pluralityof pulse shaping functions, wherein the received CPM signal isapproximated with a dominant pulse shaping function having largestenergy among the plurality of pulse shaping functions, and wherein theapproximation error is estimated based on a parameter indicative of aratio of energy of the dominant pulse shaping function to energy ofremaining ones of the plurality of pulse shaping functions.
 24. Theapparatus of claim 21, further comprising: a channel estimator operativeto derive a channel impulse response estimate based on the received CPMsignal, and wherein the signal power and the noise power are estimatedbased on the channel impulse response estimate.
 25. An apparatusoperable to estimate signal quality (C/I) in a wireless communicationsystem utilizing continuous phase modulation (CPM), comprising: meansfor estimating signal power in a received CPM signal; means forestimating noise power in the received CPM signal; means for estimatingerror due to approximation of the received CPM signal with anothermodulation format different from CPM; and means for deriving a C/Iestimate for the received CPM signal based on the estimated signalpower, the estimated noise power, and the estimated approximation error.26. The apparatus of claim 25, wherein the received CPM signal isapproximated as a phase shift keying (PSK) modulated signal, and whereinthe signal power and the noise power are estimated based on theapproximation of the received CPM signal as a PSK modulated signal. 27.The apparatus of claim 25, wherein the received CPM signal is formedbased on a plurality of pulse shaping functions, wherein the receivedCPM signal is approximated with a dominant pulse shaping function havinglargest energy among the plurality of pulse shaping functions, andwherein the approximation error is estimated based on a parameterindicative of a ratio of energy of the dominant pulse shaping functionto energy of remaining ones of the plurality of pulse shaping functions.28. The apparatus of claim 25, further comprising: means for deriving achannel impulse response estimate based on the received CPM signal, andwherein the signal power and the noise power are estimated based on thechannel impulse response estimate.
 29. A processor readable media forstoring instructions operable in a wireless device to: estimate signalpower in a received continuous phase modulation (CPM) signal; estimatenoise power in the received CPM signal; estimate error due toapproximation of the received CPM signal with another modulation formatdifferent from CPM; and derive a signal quality (C/I) estimate for thereceived CPM signal based on the estimated signal power, the estimatednoise power, and the estimated approximation error.